1. Technical Field
The present application relates generally to an improved data processing system and method. More specifically, the present application is directed to a system and method for correlated analysis of wasted space and capacity efficiency in complex storage infrastructures.
2. Description of Related Art
Enterprise Storage Resource Management (SRM) solutions have emerged to assist storage administrators with the overwhelming operational tasks of managing today's complex storage environments. One area of great concern to companies is capacity management of the storage infrastructure. Capacity management includes discovering, monitoring, reporting, planning, and provisioning of storage resources in order to guarantee necessary storage resources are available for on-going business operations. However, known SRM solutions are not as efficient as would be desired at providing such capacity management functionality.
Known SRM capacity management mechanisms provide extensive reporting and monitoring capabilities across various components of the storage infrastructure. Reporting capabilities include measurements of total, used, and free storage capacities. Historical views of these values may be typically reported as well as future trending reports. Moreover, reporting capabilities of known SRM capacity management mechanisms also support monitoring of some or all of these values for comparison to utilization thresholds such that events may be triggered, notifications may be sent, or, in more advanced solutions, automated actions may be performed. While SRM solutions have been evolving for several years, actual full scale deployment of SRM solutions in large, heterogeneous, distributed enterprises is a much more recent occurrence. These large scale deployments are revealing the limitations of existing SRM solutions.
The majority of known SRM systems require separate SRM tools for each component of the storage infrastructure, e.g., one tool for host server based capacity planning, another tool for network capacity planning, and others for storage subsystem capacity management, etc. As a result, reporting of information is provided in a separate manner and a storage administrator must access multiple reports and attempt to correlate the information himself/herself to obtain an overall view of the storage system situation.
In the most modern SRM systems, a single tool is used to perform capacity reporting and monitoring for all of the storage infrastructure components of a complex storage infrastructure, i.e. applications, file systems, databases, volumes, host based volume managers, networks, storage networks, disk storage subsystems, and tape storage subsystems. In addition, modern SRM systems have started to move past data collection, reporting and monitoring, into analysis of the data. However, while a single tool is provided, the reporting and management aspects are still separated for each storage infrastructure component.
Moreover, some SRM tools also support automated actions based on certain storage capacity events, e.g., a file system or database running out of space. However, the automated actions performed by known SRM tools are event-based and thus, are only performed in a reactionary manner once a predetermined event has occurred. That is, once a condition of a storage infrastructure component exceeds a threshold, i.e. the event occurs, only then will an automated action be performed based on the detected condition. There is no ability to proactively perform automated actions to avoid such events.
Despite the evolution of SRM solutions described above, storage capacity management today continues to be inefficient, this inefficiency being driven by the high growth rates of file and database data. In addition, while now available through a single tool, capacity reports are still typically segregated by components of the storage infrastructure, offering storage administrators raw data that is largely unusable. Furthermore, many storage provisioning and management tasks are manual and thus, consume a large amount of time that results in information technology that is not as responsive to the needs of businesses as desired. Thus, while storage capacity management solutions are evolving, they are not keeping pace with data growth and management requirements. As a result, storage administrators spend a tremendous amount of time performing manual capacity management tasks. These factors result in a situation where information technology resources, such as servers, storage subsystems, and networks, remain underutilized and inefficiently managed while people resources are over-utilized.